Whether studying zebrafish patterns or another biological system, the first step to relating modeling frameworks at large scale is quantitatively describing self-organization. Methods for quantifying in vivo and in silico patterns include pair-correlation functions [1]}, [2]}, [3]}, order parameters [4]}, [5]}, [6]}, and pattern-simplicity scores [7]}, [8]}. Recently tools from topological data analysis—particularly persistent homology—[9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]} have also emerged as a flexible means of quantifying complex systems and pattern formation [17]}, [18]}, [19]}. Persistent homology has been applied to many biological systems, including flocking [17]}, [21]}, vascular networks [22]}, [23]}, [24]}, migrating cells [25]}, [26]}, intracellular dynamics [27]}, and fish skin [28]}.
